$0.09 via x402: top stablecoin DeFi yields vs official US T-bill rate — spreads + verdict (TradFi x DeFi in one call).
| Name | Required | Description | Default |
|---|---|---|---|
| min_tvl | No | Minimum pool TVL USD, default 10000000 | |
| x_payment | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description partially carries the transparency burden. It discloses the comparison nature, pricing ($0.09 via x402), and output type ('spreads + verdict'), but omits details like data sources, update frequency, or how 'top' is determined. This is useful but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that packs in purpose, pricing, and outcome. It is front-loaded and free of fluff, though the 'TradFi x DeFi' tagline is mildly jargon-heavy. Still, it earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema and no annotations, the description provides only a high-level overview. It does not explain what a 'verdict' looks like, how to interpret the spread, or how to use the parameters effectively. For a tool that likely returns a complex comparison, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 50% (min_tvl described, x_payment not). The description adds no parameter-level explanation, leaving x_payment completely ambiguous. It does not compensate for the uncovered parameter, and even for min_tvl it repeats the schema's default rather than explaining impact on results.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: comparing top stablecoin DeFi yields to the official US T-bill rate, with spreads and a verdict. This specific verb+resource structure distinguishes it from sibling tools like defi_yields or us_treasury_rates, which cover only one side.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case (comparing TradFi vs DeFi returns) but does not explicitly state when to use this tool over alternatives. No mention of exclusions or sibling tools, though the value proposition is clear enough to infer a primary scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.
Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.
At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.
The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.